Convolutional Neural Network for Brain MR Imaging Extraction Using Silver-Standards Masks
(2018)

Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required for the training stage. Usually, manual annotation is considered to be the “gold-standard’. However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. Moreover, the single-rater manual annotation is often used in data-driven approaches making the algorithm optimal for one guideline only. In this work, we propose a convolutional neural network (CNN) for brain magnetic resonance (MR) imaging extraction, fully trained with what we refer to as silver-standard masks. To the best of our knowledge, our approach is the first deep learning approach that is fully trained using silver-standards masks, having an optimal generalization. Furthermore, regarding the Dice coefficient, we outperform the current skull-stripping (SS) state-of-the-art method in three datasets. Our method consists of 1) a dataset with silver-standard masks as input, 2) a tri-planar method using parallel 2D U-Net-based CNNs, which we refer to as CONSNet, and 3) an auto-context implementation of CONSNet. CONSNet refers to our integrated approach, i.e., training with silver-standard masks and us∗Corresponding author Email address: oeslle@dca.fee.unicamp.br (Oeslle Lucena) URL: miclab.fee.unicamp.br (Oeslle Lucena) Preprint submitted to Medical Image Analysis April 13, 2018 ar X iv :1 80 4. 04 98 8v 1 [ cs .C V ] 1 3 A pr 2 01 8 ing a CNN architecture. Masks are generated by forming a consensus from a set of publicly available automatic segmentation methods using the simultaneous truth and performance level estimation (STAPLE) algorithm. The CNN architecture is robust, with dropout and batch normalization as regularizers. It was inspired by the 2D U-Net model published by the RECOD group. We conducted our analysis using three publicly available datasets: the Calgary-Campinas-359 (CC-359 ), the LONI Probabilistic Brain Atlas (LPBA40), and the Open Access Series of Imaging Studies (OASIS). Five performance metrics were used in our experiments: Dice coefficient, sensitivity, specificity, Hausdorff distance, and symmetric surface-to-surface mean distance. Our results also demonstrate that we have comparable overall performance versus current methods, including recently proposed deep learningbased strategies that were trained using manually annotated masks. Our usage of silver-standard masks, however, reduced the cost of manual annotation, diminished inter-/intra-rater variability, and avoided CNN segmentation super-specialization towards one specific SS method found when mask generation is done through an agreement among many automatic methods. Moreover, usage of silver-standard masks enlarges the volume of input annotated data since we can generate annotation from unlabelled data. Also, our method has the advantage that, once trained, it takes only a few seconds to process a typical brain image volume using a modern graphics processing unit (GPU). In contrast, many of the other competitive consensus-building methods have processing times on the order of minutes.